Overview

Dataset statistics

Number of variables31
Number of observations1625
Missing cells4
Missing cells (%)< 0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.2 MiB
Average record size in memory796.8 B

Variable types

Categorical14
Numeric16
Boolean1

Alerts

game_id has a high cardinality: 820 distinct values High cardinality
player has a high cardinality: 66 distinct values High cardinality
game_date has a high cardinality: 174 distinct values High cardinality
fga is highly correlated with fgm and 5 other fieldsHigh correlation
fgm is highly correlated with fga and 5 other fieldsHigh correlation
xpa is highly correlated with xpmHigh correlation
xpm is highly correlated with xpaHigh correlation
fga_0_39 is highly correlated with fga and 5 other fieldsHigh correlation
fgm_0_39 is highly correlated with fga and 5 other fieldsHigh correlation
fga_40_49 is highly correlated with fgm_40_49High correlation
fgm_40_49 is highly correlated with fga_40_49 and 3 other fieldsHigh correlation
fga_50 is highly correlated with fgm_50High correlation
fgm_50 is highly correlated with fga_50High correlation
Total_DKP is highly correlated with fga and 6 other fieldsHigh correlation
Total_FDP is highly correlated with fga and 6 other fieldsHigh correlation
Total_SDP is highly correlated with fga and 6 other fieldsHigh correlation
fga is highly correlated with fgm and 5 other fieldsHigh correlation
fgm is highly correlated with fga and 5 other fieldsHigh correlation
xpa is highly correlated with xpmHigh correlation
xpm is highly correlated with xpaHigh correlation
fga_0_39 is highly correlated with fga and 5 other fieldsHigh correlation
fgm_0_39 is highly correlated with fga and 5 other fieldsHigh correlation
fga_40_49 is highly correlated with fgm_40_49High correlation
fgm_40_49 is highly correlated with fga_40_49 and 3 other fieldsHigh correlation
fga_50 is highly correlated with fgm_50High correlation
fgm_50 is highly correlated with fga_50High correlation
Total_DKP is highly correlated with fga and 6 other fieldsHigh correlation
Total_FDP is highly correlated with fga and 6 other fieldsHigh correlation
Total_SDP is highly correlated with fga and 6 other fieldsHigh correlation
fga is highly correlated with fgm and 4 other fieldsHigh correlation
fgm is highly correlated with fga and 5 other fieldsHigh correlation
xpa is highly correlated with xpmHigh correlation
xpm is highly correlated with xpaHigh correlation
fga_0_39 is highly correlated with fga and 2 other fieldsHigh correlation
fgm_0_39 is highly correlated with fgm and 1 other fieldsHigh correlation
fga_40_49 is highly correlated with fgm_40_49High correlation
fgm_40_49 is highly correlated with fga_40_49High correlation
fga_50 is highly correlated with fgm_50High correlation
fgm_50 is highly correlated with fga_50High correlation
Total_DKP is highly correlated with fga and 3 other fieldsHigh correlation
Total_FDP is highly correlated with fga and 3 other fieldsHigh correlation
Total_SDP is highly correlated with fga and 3 other fieldsHigh correlation
home_team is highly correlated with Vegas_Favorite and 2 other fieldsHigh correlation
player is highly correlated with Off_abbrevHigh correlation
Vegas_Favorite is highly correlated with home_teamHigh correlation
fga_50 is highly correlated with fgm_50High correlation
Surface is highly correlated with home_teamHigh correlation
Roof is highly correlated with home_teamHigh correlation
fgm_50 is highly correlated with fga_50High correlation
fga_40_49 is highly correlated with fgm_40_49High correlation
Off_abbrev is highly correlated with playerHigh correlation
fgm_40_49 is highly correlated with fga_40_49High correlation
Off_abbrev is highly correlated with Def_abbrev and 8 other fieldsHigh correlation
Def_abbrev is highly correlated with Off_abbrev and 8 other fieldsHigh correlation
fga is highly correlated with Off_abbrev and 8 other fieldsHigh correlation
fgm is highly correlated with Off_abbrev and 9 other fieldsHigh correlation
xpa is highly correlated with xpm and 2 other fieldsHigh correlation
xpm is highly correlated with xpa and 2 other fieldsHigh correlation
fga_0_39 is highly correlated with fga and 5 other fieldsHigh correlation
fgm_0_39 is highly correlated with fga and 5 other fieldsHigh correlation
fga_40_49 is highly correlated with fga and 4 other fieldsHigh correlation
fgm_40_49 is highly correlated with fgm and 4 other fieldsHigh correlation
fga_50 is highly correlated with fgm_50High correlation
fgm_50 is highly correlated with fgm and 4 other fieldsHigh correlation
player is highly correlated with Off_abbrev and 6 other fieldsHigh correlation
Total_DKP is highly correlated with fga and 8 other fieldsHigh correlation
Total_FDP is highly correlated with fga and 8 other fieldsHigh correlation
Total_SDP is highly correlated with fga and 8 other fieldsHigh correlation
vis_team is highly correlated with Off_abbrev and 4 other fieldsHigh correlation
home_team is highly correlated with Off_abbrev and 10 other fieldsHigh correlation
vis_score is highly correlated with xpa and 1 other fieldsHigh correlation
home_score is highly correlated with xpa and 1 other fieldsHigh correlation
Roof is highly correlated with Off_abbrev and 8 other fieldsHigh correlation
Surface is highly correlated with Off_abbrev and 5 other fieldsHigh correlation
Temperature is highly correlated with home_team and 4 other fieldsHigh correlation
Humidity is highly correlated with home_team and 3 other fieldsHigh correlation
Wind_Speed is highly correlated with home_team and 3 other fieldsHigh correlation
Vegas_Favorite is highly correlated with Off_abbrev and 10 other fieldsHigh correlation
Over_Under is highly correlated with home_team and 1 other fieldsHigh correlation
game_id is uniformly distributed Uniform
vis_team is uniformly distributed Uniform
home_team is uniformly distributed Uniform
fga has 231 (14.2%) zeros Zeros
fgm has 285 (17.5%) zeros Zeros
xpa has 143 (8.8%) zeros Zeros
xpm has 178 (11.0%) zeros Zeros
fga_0_39 has 649 (39.9%) zeros Zeros
fgm_0_39 has 678 (41.7%) zeros Zeros
Total_DKP has 29 (1.8%) zeros Zeros
Total_FDP has 29 (1.8%) zeros Zeros
Total_SDP has 29 (1.8%) zeros Zeros
Wind_Speed has 589 (36.2%) zeros Zeros

Reproduction

Analysis started2022-09-02 02:18:17.379968
Analysis finished2022-09-02 02:19:24.022107
Duration1 minute and 6.64 seconds
Software versionpandas-profiling v3.2.0
Download configurationconfig.json

Variables

game_id
Categorical

HIGH CARDINALITY
UNIFORM

Distinct820
Distinct (%)50.5%
Missing0
Missing (%)0.0%
Memory size109.6 KiB
202110100tam
 
3
202012270nyj
 
3
202109120nwe
 
2
202102070tam
 
2
202109090tam
 
2
Other values (815)
1613 

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters19500
Distinct characters34
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique17 ?
Unique (%)1.0%

Sample

1st row201909050chi
2nd row201909050chi
3rd row201909080car
4th row201909080car
5th row201909080cle

Common Values

ValueCountFrequency (%)
202110100tam3
 
0.2%
202012270nyj3
 
0.2%
202109120nwe2
 
0.1%
202102070tam2
 
0.1%
202109090tam2
 
0.1%
202109120atl2
 
0.1%
202109120buf2
 
0.1%
202109120cin2
 
0.1%
202109120clt2
 
0.1%
202109120det2
 
0.1%
Other values (810)1603
98.6%

Length

2022-09-02T02:19:24.218580image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
202110100tam3
 
0.2%
202012270nyj3
 
0.2%
201909080nwe2
 
0.1%
201909290buf2
 
0.1%
201909150ram2
 
0.1%
201909150rai2
 
0.1%
201909150pit2
 
0.1%
201909150oti2
 
0.1%
201909080cle2
 
0.1%
201909080crd2
 
0.1%
Other values (810)1603
98.6%

Most occurring characters

ValueCountFrequency (%)
05230
26.8%
24006
20.5%
13402
17.4%
91013
 
5.2%
a615
 
3.2%
n471
 
2.4%
i400
 
2.1%
t357
 
1.8%
r302
 
1.5%
c296
 
1.5%
Other values (24)3408
17.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number14625
75.0%
Lowercase Letter4875
 
25.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a615
 
12.6%
n471
 
9.7%
i400
 
8.2%
t357
 
7.3%
r302
 
6.2%
c296
 
6.1%
e248
 
5.1%
d243
 
5.0%
m202
 
4.1%
s201
 
4.1%
Other values (14)1540
31.6%
Decimal Number
ValueCountFrequency (%)
05230
35.8%
24006
27.4%
13402
23.3%
91013
 
6.9%
3224
 
1.5%
7176
 
1.2%
5161
 
1.1%
8154
 
1.1%
6137
 
0.9%
4122
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
Common14625
75.0%
Latin4875
 
25.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a615
 
12.6%
n471
 
9.7%
i400
 
8.2%
t357
 
7.3%
r302
 
6.2%
c296
 
6.1%
e248
 
5.1%
d243
 
5.0%
m202
 
4.1%
s201
 
4.1%
Other values (14)1540
31.6%
Common
ValueCountFrequency (%)
05230
35.8%
24006
27.4%
13402
23.3%
91013
 
6.9%
3224
 
1.5%
7176
 
1.2%
5161
 
1.1%
8154
 
1.1%
6137
 
0.9%
4122
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII19500
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
05230
26.8%
24006
20.5%
13402
17.4%
91013
 
5.2%
a615
 
3.2%
n471
 
2.4%
i400
 
2.1%
t357
 
1.8%
r302
 
1.5%
c296
 
1.5%
Other values (24)3408
17.5%

Off_abbrev
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct32
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size94.9 KiB
0
231 
TAM
 
53
LAR
 
53
CIN
 
52
SFO
 
49
Other values (27)
1187 

Length

Max length3
Median length3
Mean length2.715692308
Min length1

Characters and Unicode

Total characters4413
Distinct characters25
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCHI
2nd rowGNB
3rd rowCAR
4th rowLAR
5th rowTEN

Common Values

ValueCountFrequency (%)
0231
 
14.2%
TAM53
 
3.3%
LAR53
 
3.3%
CIN52
 
3.2%
SFO49
 
3.0%
KAN49
 
3.0%
BAL48
 
3.0%
LVR48
 
3.0%
NWE48
 
3.0%
BUF48
 
3.0%
Other values (22)946
58.2%

Length

2022-09-02T02:19:24.442231image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
0231
 
14.2%
lar53
 
3.3%
tam53
 
3.3%
cin52
 
3.2%
sfo49
 
3.0%
kan49
 
3.0%
bal48
 
3.0%
lvr48
 
3.0%
buf48
 
3.0%
nwe48
 
3.0%
Other values (22)946
58.2%

Most occurring characters

ValueCountFrequency (%)
A592
13.4%
N486
 
11.0%
L323
 
7.3%
I318
 
7.2%
E260
 
5.9%
R235
 
5.3%
0231
 
5.2%
T230
 
5.2%
C223
 
5.1%
D176
 
4.0%
Other values (15)1339
30.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter4182
94.8%
Decimal Number231
 
5.2%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A592
14.2%
N486
11.6%
L323
 
7.7%
I318
 
7.6%
E260
 
6.2%
R235
 
5.6%
T230
 
5.5%
C223
 
5.3%
D176
 
4.2%
O138
 
3.3%
Other values (14)1201
28.7%
Decimal Number
ValueCountFrequency (%)
0231
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin4182
94.8%
Common231
 
5.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
A592
14.2%
N486
11.6%
L323
 
7.7%
I318
 
7.6%
E260
 
6.2%
R235
 
5.6%
T230
 
5.5%
C223
 
5.3%
D176
 
4.2%
O138
 
3.3%
Other values (14)1201
28.7%
Common
ValueCountFrequency (%)
0231
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII4413
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A592
13.4%
N486
 
11.0%
L323
 
7.3%
I318
 
7.2%
E260
 
5.9%
R235
 
5.3%
0231
 
5.2%
T230
 
5.2%
C223
 
5.1%
D176
 
4.0%
Other values (15)1339
30.3%

Def_abbrev
Categorical

HIGH CORRELATION

Distinct33
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size94.9 KiB
0
231 
TAM
 
52
LAR
 
48
HOU
 
47
KAN
 
47
Other values (28)
1200 

Length

Max length3
Median length3
Mean length2.715692308
Min length1

Characters and Unicode

Total characters4413
Distinct characters25
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGNB
2nd rowCHI
3rd rowLAR
4th rowCAR
5th rowCLE

Common Values

ValueCountFrequency (%)
0231
 
14.2%
TAM52
 
3.2%
LAR48
 
3.0%
HOU47
 
2.9%
KAN47
 
2.9%
GNB46
 
2.8%
MIN46
 
2.8%
CIN46
 
2.8%
SFO46
 
2.8%
JAX45
 
2.8%
Other values (23)971
59.8%

Length

2022-09-02T02:19:24.684254image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
0231
 
14.2%
tam52
 
3.2%
lar48
 
3.0%
hou47
 
2.9%
kan47
 
2.9%
gnb46
 
2.8%
min46
 
2.8%
cin46
 
2.8%
sfo46
 
2.8%
jax45
 
2.8%
Other values (23)971
59.8%

Most occurring characters

ValueCountFrequency (%)
A567
12.8%
N481
 
10.9%
I355
 
8.0%
L299
 
6.8%
E253
 
5.7%
0231
 
5.2%
T221
 
5.0%
R220
 
5.0%
C217
 
4.9%
D163
 
3.7%
Other values (15)1406
31.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter4182
94.8%
Decimal Number231
 
5.2%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A567
13.6%
N481
 
11.5%
I355
 
8.5%
L299
 
7.1%
E253
 
6.0%
T221
 
5.3%
R220
 
5.3%
C217
 
5.2%
D163
 
3.9%
M143
 
3.4%
Other values (14)1263
30.2%
Decimal Number
ValueCountFrequency (%)
0231
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin4182
94.8%
Common231
 
5.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
A567
13.6%
N481
 
11.5%
I355
 
8.5%
L299
 
7.1%
E253
 
6.0%
T221
 
5.3%
R220
 
5.3%
C217
 
5.2%
D163
 
3.9%
M143
 
3.4%
Other values (14)1263
30.2%
Common
ValueCountFrequency (%)
0231
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII4413
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A567
12.8%
N481
 
10.9%
I355
 
8.0%
L299
 
6.8%
E253
 
5.7%
0231
 
5.2%
T221
 
5.0%
R220
 
5.0%
C217
 
4.9%
D163
 
3.7%
Other values (15)1406
31.9%

fga
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct9
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.861538462
Minimum0
Maximum8
Zeros231
Zeros (%)14.2%
Negative0
Negative (%)0.0%
Memory size12.8 KiB
2022-09-02T02:19:24.876781image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q33
95-th percentile4
Maximum8
Range8
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.281222417
Coefficient of variation (CV)0.6882599763
Kurtosis0.2141970194
Mean1.861538462
Median Absolute Deviation (MAD)1
Skewness0.5378139105
Sum3025
Variance1.641530883
MonotonicityNot monotonic
2022-09-02T02:19:25.066089image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
1473
29.1%
2440
27.1%
3299
18.4%
0231
14.2%
4147
 
9.0%
528
 
1.7%
64
 
0.2%
82
 
0.1%
71
 
0.1%
ValueCountFrequency (%)
0231
14.2%
1473
29.1%
2440
27.1%
3299
18.4%
4147
 
9.0%
528
 
1.7%
64
 
0.2%
71
 
0.1%
82
 
0.1%
ValueCountFrequency (%)
82
 
0.1%
71
 
0.1%
64
 
0.2%
528
 
1.7%
4147
 
9.0%
3299
18.4%
2440
27.1%
1473
29.1%
0231
14.2%

fgm
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct8
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.604307692
Minimum0
Maximum7
Zeros285
Zeros (%)17.5%
Negative0
Negative (%)0.0%
Memory size12.8 KiB
2022-09-02T02:19:25.252290image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q32
95-th percentile4
Maximum7
Range7
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.173540873
Coefficient of variation (CV)0.7314936398
Kurtosis0.08298606242
Mean1.604307692
Median Absolute Deviation (MAD)1
Skewness0.5861799492
Sum2607
Variance1.377198181
MonotonicityNot monotonic
2022-09-02T02:19:25.437835image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
1557
34.3%
2427
26.3%
0285
17.5%
3249
15.3%
491
 
5.6%
512
 
0.7%
63
 
0.2%
71
 
0.1%
ValueCountFrequency (%)
0285
17.5%
1557
34.3%
2427
26.3%
3249
15.3%
491
 
5.6%
512
 
0.7%
63
 
0.2%
71
 
0.1%
ValueCountFrequency (%)
71
 
0.1%
63
 
0.2%
512
 
0.7%
491
 
5.6%
3249
15.3%
2427
26.3%
1557
34.3%
0285
17.5%

xpa
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct9
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.445538462
Minimum0
Maximum8
Zeros143
Zeros (%)8.8%
Negative0
Negative (%)0.0%
Memory size12.8 KiB
2022-09-02T02:19:25.640813image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q33
95-th percentile5
Maximum8
Range8
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.488343492
Coefficient of variation (CV)0.6085954139
Kurtosis0.006124483226
Mean2.445538462
Median Absolute Deviation (MAD)1
Skewness0.4289574424
Sum3974
Variance2.215166351
MonotonicityNot monotonic
2022-09-02T02:19:25.835300image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
2422
26.0%
3369
22.7%
1314
19.3%
4233
14.3%
0143
 
8.8%
5103
 
6.3%
628
 
1.7%
710
 
0.6%
83
 
0.2%
ValueCountFrequency (%)
0143
 
8.8%
1314
19.3%
2422
26.0%
3369
22.7%
4233
14.3%
5103
 
6.3%
628
 
1.7%
710
 
0.6%
83
 
0.2%
ValueCountFrequency (%)
83
 
0.2%
710
 
0.6%
628
 
1.7%
5103
 
6.3%
4233
14.3%
3369
22.7%
2422
26.0%
1314
19.3%
0143
 
8.8%

xpm
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct9
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.286153846
Minimum0
Maximum8
Zeros178
Zeros (%)11.0%
Negative0
Negative (%)0.0%
Memory size12.8 KiB
2022-09-02T02:19:26.035965image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q33
95-th percentile5
Maximum8
Range8
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.474315918
Coefficient of variation (CV)0.6448891971
Kurtosis-0.07748183724
Mean2.286153846
Median Absolute Deviation (MAD)1
Skewness0.4468409697
Sum3715
Variance2.173607427
MonotonicityNot monotonic
2022-09-02T02:19:26.234784image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
2415
25.5%
1353
21.7%
3346
21.3%
4214
13.2%
0178
11.0%
585
 
5.2%
628
 
1.7%
83
 
0.2%
73
 
0.2%
ValueCountFrequency (%)
0178
11.0%
1353
21.7%
2415
25.5%
3346
21.3%
4214
13.2%
585
 
5.2%
628
 
1.7%
73
 
0.2%
83
 
0.2%
ValueCountFrequency (%)
83
 
0.2%
73
 
0.2%
628
 
1.7%
585
 
5.2%
4214
13.2%
3346
21.3%
2415
25.5%
1353
21.7%
0178
11.0%

fga_0_39
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct7
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.9224615385
Minimum0
Maximum6
Zeros649
Zeros (%)39.9%
Negative0
Negative (%)0.0%
Memory size12.8 KiB
2022-09-02T02:19:26.426200image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q31
95-th percentile3
Maximum6
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.9675238041
Coefficient of variation (CV)1.048850021
Kurtosis1.1717699
Mean0.9224615385
Median Absolute Deviation (MAD)1
Skewness1.070343411
Sum1499
Variance0.9361023115
MonotonicityNot monotonic
2022-09-02T02:19:26.627308image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0649
39.9%
1594
36.6%
2273
16.8%
382
 
5.0%
423
 
1.4%
53
 
0.2%
61
 
0.1%
ValueCountFrequency (%)
0649
39.9%
1594
36.6%
2273
16.8%
382
 
5.0%
423
 
1.4%
53
 
0.2%
61
 
0.1%
ValueCountFrequency (%)
61
 
0.1%
53
 
0.2%
423
 
1.4%
382
 
5.0%
2273
16.8%
1594
36.6%
0649
39.9%

fgm_0_39
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct6
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.8750769231
Minimum0
Maximum5
Zeros678
Zeros (%)41.7%
Negative0
Negative (%)0.0%
Memory size12.8 KiB
2022-09-02T02:19:26.803823image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q31
95-th percentile3
Maximum5
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.931668023
Coefficient of variation (CV)1.064669857
Kurtosis0.8034670667
Mean0.8750769231
Median Absolute Deviation (MAD)1
Skewness1.014299913
Sum1422
Variance0.868005305
MonotonicityNot monotonic
2022-09-02T02:19:26.989272image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0678
41.7%
1589
36.2%
2263
 
16.2%
375
 
4.6%
418
 
1.1%
52
 
0.1%
ValueCountFrequency (%)
0678
41.7%
1589
36.2%
2263
 
16.2%
375
 
4.6%
418
 
1.1%
52
 
0.1%
ValueCountFrequency (%)
52
 
0.1%
418
 
1.1%
375
 
4.6%
2263
 
16.2%
1589
36.2%
0678
41.7%

fga_40_49
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct5
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size92.2 KiB
0
932 
1
519 
2
144 
3
 
27
4
 
3

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1625
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row2
5th row0

Common Values

ValueCountFrequency (%)
0932
57.4%
1519
31.9%
2144
 
8.9%
327
 
1.7%
43
 
0.2%

Length

2022-09-02T02:19:27.200375image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-02T02:19:27.428215image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
0932
57.4%
1519
31.9%
2144
 
8.9%
327
 
1.7%
43
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0932
57.4%
1519
31.9%
2144
 
8.9%
327
 
1.7%
43
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1625
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0932
57.4%
1519
31.9%
2144
 
8.9%
327
 
1.7%
43
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Common1625
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0932
57.4%
1519
31.9%
2144
 
8.9%
327
 
1.7%
43
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII1625
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0932
57.4%
1519
31.9%
2144
 
8.9%
327
 
1.7%
43
 
0.2%

fgm_40_49
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct4
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size92.2 KiB
0
1063 
1
448 
2
 
98
3
 
16

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1625
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
01063
65.4%
1448
27.6%
298
 
6.0%
316
 
1.0%

Length

2022-09-02T02:19:27.611897image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-02T02:19:27.847771image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
01063
65.4%
1448
27.6%
298
 
6.0%
316
 
1.0%

Most occurring characters

ValueCountFrequency (%)
01063
65.4%
1448
27.6%
298
 
6.0%
316
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1625
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
01063
65.4%
1448
27.6%
298
 
6.0%
316
 
1.0%

Most occurring scripts

ValueCountFrequency (%)
Common1625
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
01063
65.4%
1448
27.6%
298
 
6.0%
316
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII1625
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
01063
65.4%
1448
27.6%
298
 
6.0%
316
 
1.0%

fga_50
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct4
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size92.2 KiB
0
1235 
1
319 
2
 
66
3
 
5

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1625
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row2
4th row1
5th row1

Common Values

ValueCountFrequency (%)
01235
76.0%
1319
 
19.6%
266
 
4.1%
35
 
0.3%

Length

2022-09-02T02:19:28.017602image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-02T02:19:28.238142image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
01235
76.0%
1319
 
19.6%
266
 
4.1%
35
 
0.3%

Most occurring characters

ValueCountFrequency (%)
01235
76.0%
1319
 
19.6%
266
 
4.1%
35
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1625
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
01235
76.0%
1319
 
19.6%
266
 
4.1%
35
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
Common1625
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
01235
76.0%
1319
 
19.6%
266
 
4.1%
35
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII1625
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
01235
76.0%
1319
 
19.6%
266
 
4.1%
35
 
0.3%

fgm_50
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct4
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size92.2 KiB
0
1366 
1
226 
2
 
30
3
 
3

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1625
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
01366
84.1%
1226
 
13.9%
230
 
1.8%
33
 
0.2%

Length

2022-09-02T02:19:28.415552image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-02T02:19:28.646547image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
01366
84.1%
1226
 
13.9%
230
 
1.8%
33
 
0.2%

Most occurring characters

ValueCountFrequency (%)
01366
84.1%
1226
 
13.9%
230
 
1.8%
33
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1625
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
01366
84.1%
1226
 
13.9%
230
 
1.8%
33
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Common1625
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
01366
84.1%
1226
 
13.9%
230
 
1.8%
33
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII1625
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
01366
84.1%
1226
 
13.9%
230
 
1.8%
33
 
0.2%

player
Categorical

HIGH CARDINALITY
HIGH CORRELATION
HIGH CORRELATION

Distinct66
Distinct (%)4.1%
Missing0
Missing (%)0.0%
Memory size110.3 KiB
Harrison Butker
 
56
Mason Crosby
 
53
Justin Tucker
 
52
Jason Myers
 
51
Dustin Hopkins
 
50
Other values (61)
1363 

Length

Max length19
Median length16
Mean length12.45476923
Min length7

Characters and Unicode

Total characters20239
Distinct characters47
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10 ?
Unique (%)0.6%

Sample

1st rowEddy Pineiro
2nd rowMason Crosby
3rd rowJoey Slye
4th rowGreg Zuerlein
5th rowCairo Santos

Common Values

ValueCountFrequency (%)
Harrison Butker56
 
3.4%
Mason Crosby53
 
3.3%
Justin Tucker52
 
3.2%
Jason Myers51
 
3.1%
Dustin Hopkins50
 
3.1%
Jake Elliott50
 
3.1%
Matt Prater50
 
3.1%
Daniel Carlson50
 
3.1%
Chris Boswell48
 
3.0%
Greg Zuerlein48
 
3.0%
Other values (56)1117
68.7%

Length

2022-09-02T02:19:28.832293image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
matt112
 
3.4%
jason98
 
3.0%
greg70
 
2.2%
harrison56
 
1.7%
butker56
 
1.7%
mason53
 
1.6%
crosby53
 
1.6%
elliott53
 
1.6%
justin52
 
1.6%
tucker52
 
1.6%
Other values (109)2595
79.8%

Most occurring characters

ValueCountFrequency (%)
a1767
 
8.7%
1625
 
8.0%
o1412
 
7.0%
n1392
 
6.9%
e1381
 
6.8%
r1202
 
5.9%
i1197
 
5.9%
s1116
 
5.5%
l925
 
4.6%
t883
 
4.4%
Other values (37)7339
36.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter15220
75.2%
Uppercase Letter3349
 
16.5%
Space Separator1625
 
8.0%
Other Punctuation45
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a1767
11.6%
o1412
9.3%
n1392
9.1%
e1381
9.1%
r1202
 
7.9%
i1197
 
7.9%
s1116
 
7.3%
l925
 
6.1%
t883
 
5.8%
u610
 
4.0%
Other values (14)3335
21.9%
Uppercase Letter
ValueCountFrequency (%)
M445
13.3%
B366
10.9%
J298
 
8.9%
S289
 
8.6%
G289
 
8.6%
C250
 
7.5%
R197
 
5.9%
D134
 
4.0%
H128
 
3.8%
P119
 
3.6%
Other values (11)834
24.9%
Space Separator
ValueCountFrequency (%)
1625
100.0%
Other Punctuation
ValueCountFrequency (%)
'45
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin18569
91.7%
Common1670
 
8.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
a1767
 
9.5%
o1412
 
7.6%
n1392
 
7.5%
e1381
 
7.4%
r1202
 
6.5%
i1197
 
6.4%
s1116
 
6.0%
l925
 
5.0%
t883
 
4.8%
u610
 
3.3%
Other values (35)6684
36.0%
Common
ValueCountFrequency (%)
1625
97.3%
'45
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII20239
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a1767
 
8.7%
1625
 
8.0%
o1412
 
7.0%
n1392
 
6.9%
e1381
 
6.8%
r1202
 
5.9%
i1197
 
5.9%
s1116
 
5.5%
l925
 
4.6%
t883
 
4.4%
Other values (37)7339
36.3%

Total_DKP
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct26
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.522461538
Minimum0
Maximum25
Zeros29
Zeros (%)1.8%
Negative0
Negative (%)0.0%
Memory size12.8 KiB
2022-09-02T02:19:29.049478image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q14
median7
Q310
95-th percentile15
Maximum25
Range25
Interquartile range (IQR)6

Descriptive statistics

Standard deviation4.359282474
Coefficient of variation (CV)0.5795021287
Kurtosis-0.005171211841
Mean7.522461538
Median Absolute Deviation (MAD)3
Skewness0.5132432102
Sum12224
Variance19.00334369
MonotonicityNot monotonic
2022-09-02T02:19:29.277109image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
6140
 
8.6%
4140
 
8.6%
7140
 
8.6%
5132
 
8.1%
9130
 
8.0%
10121
 
7.4%
3112
 
6.9%
8110
 
6.8%
292
 
5.7%
1288
 
5.4%
Other values (16)420
25.8%
ValueCountFrequency (%)
029
 
1.8%
185
5.2%
292
5.7%
3112
6.9%
4140
8.6%
5132
8.1%
6140
8.6%
7140
8.6%
8110
6.8%
9130
8.0%
ValueCountFrequency (%)
251
 
0.1%
242
 
0.1%
231
 
0.1%
222
 
0.1%
214
 
0.2%
203
 
0.2%
192
 
0.1%
1810
 
0.6%
1715
0.9%
1634
2.1%

Total_FDP
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct26
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.522461538
Minimum0
Maximum25
Zeros29
Zeros (%)1.8%
Negative0
Negative (%)0.0%
Memory size12.8 KiB
2022-09-02T02:19:29.486985image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q14
median7
Q310
95-th percentile15
Maximum25
Range25
Interquartile range (IQR)6

Descriptive statistics

Standard deviation4.359282474
Coefficient of variation (CV)0.5795021287
Kurtosis-0.005171211841
Mean7.522461538
Median Absolute Deviation (MAD)3
Skewness0.5132432102
Sum12224
Variance19.00334369
MonotonicityNot monotonic
2022-09-02T02:19:29.713699image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
6140
 
8.6%
4140
 
8.6%
7140
 
8.6%
5132
 
8.1%
9130
 
8.0%
10121
 
7.4%
3112
 
6.9%
8110
 
6.8%
292
 
5.7%
1288
 
5.4%
Other values (16)420
25.8%
ValueCountFrequency (%)
029
 
1.8%
185
5.2%
292
5.7%
3112
6.9%
4140
8.6%
5132
8.1%
6140
8.6%
7140
8.6%
8110
6.8%
9130
8.0%
ValueCountFrequency (%)
251
 
0.1%
242
 
0.1%
231
 
0.1%
222
 
0.1%
214
 
0.2%
203
 
0.2%
192
 
0.1%
1810
 
0.6%
1715
0.9%
1634
2.1%

Total_SDP
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct26
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.522461538
Minimum0
Maximum25
Zeros29
Zeros (%)1.8%
Negative0
Negative (%)0.0%
Memory size12.8 KiB
2022-09-02T02:19:29.931860image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q14
median7
Q310
95-th percentile15
Maximum25
Range25
Interquartile range (IQR)6

Descriptive statistics

Standard deviation4.359282474
Coefficient of variation (CV)0.5795021287
Kurtosis-0.005171211841
Mean7.522461538
Median Absolute Deviation (MAD)3
Skewness0.5132432102
Sum12224
Variance19.00334369
MonotonicityNot monotonic
2022-09-02T02:19:30.158736image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
6140
 
8.6%
4140
 
8.6%
7140
 
8.6%
5132
 
8.1%
9130
 
8.0%
10121
 
7.4%
3112
 
6.9%
8110
 
6.8%
292
 
5.7%
1288
 
5.4%
Other values (16)420
25.8%
ValueCountFrequency (%)
029
 
1.8%
185
5.2%
292
5.7%
3112
6.9%
4140
8.6%
5132
8.1%
6140
8.6%
7140
8.6%
8110
6.8%
9130
8.0%
ValueCountFrequency (%)
251
 
0.1%
242
 
0.1%
231
 
0.1%
222
 
0.1%
214
 
0.2%
203
 
0.2%
192
 
0.1%
1810
 
0.6%
1715
0.9%
1634
2.1%

vis_team
Categorical

HIGH CORRELATION
UNIFORM

Distinct32
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size95.3 KiB
TAM
 
58
LAR
 
58
SFO
 
56
TEN
 
53
CLE
 
53
Other values (27)
1347 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4875
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGNB
2nd rowGNB
3rd rowLAR
4th rowLAR
5th rowTEN

Common Values

ValueCountFrequency (%)
TAM58
 
3.6%
LAR58
 
3.6%
SFO56
 
3.4%
TEN53
 
3.3%
CLE53
 
3.3%
SEA53
 
3.3%
MIN53
 
3.3%
CIN52
 
3.2%
ARI52
 
3.2%
CHI52
 
3.2%
Other values (22)1085
66.8%

Length

2022-09-02T02:19:30.375451image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
tam58
 
3.6%
lar58
 
3.6%
sfo56
 
3.4%
ten53
 
3.3%
cle53
 
3.3%
sea53
 
3.3%
min53
 
3.3%
cin52
 
3.2%
chi52
 
3.2%
phi52
 
3.2%
Other values (22)1085
66.8%

Most occurring characters

ValueCountFrequency (%)
A662
13.6%
N547
 
11.2%
I409
 
8.4%
L359
 
7.4%
E304
 
6.2%
T260
 
5.3%
R259
 
5.3%
C254
 
5.2%
D196
 
4.0%
M160
 
3.3%
Other values (14)1465
30.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter4875
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A662
13.6%
N547
 
11.2%
I409
 
8.4%
L359
 
7.4%
E304
 
6.2%
T260
 
5.3%
R259
 
5.3%
C254
 
5.2%
D196
 
4.0%
M160
 
3.3%
Other values (14)1465
30.1%

Most occurring scripts

ValueCountFrequency (%)
Latin4875
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A662
13.6%
N547
 
11.2%
I409
 
8.4%
L359
 
7.4%
E304
 
6.2%
T260
 
5.3%
R259
 
5.3%
C254
 
5.2%
D196
 
4.0%
M160
 
3.3%
Other values (14)1465
30.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII4875
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A662
13.6%
N547
 
11.2%
I409
 
8.4%
L359
 
7.4%
E304
 
6.2%
T260
 
5.3%
R259
 
5.3%
C254
 
5.2%
D196
 
4.0%
M160
 
3.3%
Other values (14)1465
30.1%

home_team
Categorical

HIGH CORRELATION
HIGH CORRELATION
UNIFORM

Distinct32
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size95.3 KiB
KAN
 
66
GNB
 
55
BUF
 
54
CIN
 
54
TAM
 
53
Other values (27)
1343 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4875
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCHI
2nd rowCHI
3rd rowCAR
4th rowCAR
5th rowCLE

Common Values

ValueCountFrequency (%)
KAN66
 
4.1%
GNB55
 
3.4%
BUF54
 
3.3%
CIN54
 
3.3%
TAM53
 
3.3%
NOR53
 
3.3%
TEN53
 
3.3%
PIT52
 
3.2%
LAR52
 
3.2%
BAL52
 
3.2%
Other values (22)1081
66.5%

Length

2022-09-02T02:19:30.575640image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
kan66
 
4.1%
gnb55
 
3.4%
buf54
 
3.3%
cin54
 
3.3%
tam53
 
3.3%
nor53
 
3.3%
ten53
 
3.3%
pit52
 
3.2%
bal52
 
3.2%
nwe52
 
3.2%
Other values (22)1081
66.5%

Most occurring characters

ValueCountFrequency (%)
A663
13.6%
N576
 
11.8%
I395
 
8.1%
L351
 
7.2%
E301
 
6.2%
T254
 
5.2%
R250
 
5.1%
C248
 
5.1%
D195
 
4.0%
B161
 
3.3%
Other values (14)1481
30.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter4875
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A663
13.6%
N576
 
11.8%
I395
 
8.1%
L351
 
7.2%
E301
 
6.2%
T254
 
5.2%
R250
 
5.1%
C248
 
5.1%
D195
 
4.0%
B161
 
3.3%
Other values (14)1481
30.4%

Most occurring scripts

ValueCountFrequency (%)
Latin4875
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A663
13.6%
N576
 
11.8%
I395
 
8.1%
L351
 
7.2%
E301
 
6.2%
T254
 
5.2%
R250
 
5.1%
C248
 
5.1%
D195
 
4.0%
B161
 
3.3%
Other values (14)1481
30.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII4875
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A663
13.6%
N576
 
11.8%
I395
 
8.1%
L351
 
7.2%
E301
 
6.2%
T254
 
5.2%
R250
 
5.1%
C248
 
5.1%
D195
 
4.0%
B161
 
3.3%
Other values (14)1481
30.4%

vis_score
Real number (ℝ≥0)

HIGH CORRELATION

Distinct50
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.25292308
Minimum0
Maximum59
Zeros9
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size12.8 KiB
2022-09-02T02:19:30.808636image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile7
Q116
median23
Q330
95-th percentile41
Maximum59
Range59
Interquartile range (IQR)14

Descriptive statistics

Standard deviation10.09478017
Coefficient of variation (CV)0.4341295127
Kurtosis-0.234750463
Mean23.25292308
Median Absolute Deviation (MAD)7
Skewness0.116837092
Sum37786
Variance101.9045866
MonotonicityNot monotonic
2022-09-02T02:19:31.058855image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17107
 
6.6%
2792
 
5.7%
2092
 
5.7%
3190
 
5.5%
2480
 
4.9%
3076
 
4.7%
1667
 
4.1%
2366
 
4.1%
2164
 
3.9%
1062
 
3.8%
Other values (40)829
51.0%
ValueCountFrequency (%)
09
 
0.6%
340
2.5%
52
 
0.1%
619
 
1.2%
738
2.3%
81
 
0.1%
942
2.6%
1062
3.8%
1112
 
0.7%
1215
 
0.9%
ValueCountFrequency (%)
592
 
0.1%
552
 
0.1%
512
 
0.1%
494
 
0.2%
486
0.4%
474
 
0.2%
462
 
0.1%
4512
0.7%
444
 
0.2%
439
0.6%

home_score
Real number (ℝ≥0)

HIGH CORRELATION

Distinct50
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.94830769
Minimum0
Maximum56
Zeros12
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size12.8 KiB
2022-09-02T02:19:31.311994image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile7
Q117
median24
Q331
95-th percentile41
Maximum56
Range56
Interquartile range (IQR)14

Descriptive statistics

Standard deviation10.05164184
Coefficient of variation (CV)0.4197224274
Kurtosis-0.05326707193
Mean23.94830769
Median Absolute Deviation (MAD)7
Skewness0.2124142326
Sum38916
Variance101.0355036
MonotonicityNot monotonic
2022-09-02T02:19:31.583032image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
24112
 
6.9%
20105
 
6.5%
17103
 
6.3%
27100
 
6.2%
2371
 
4.4%
3171
 
4.4%
1370
 
4.3%
3466
 
4.1%
1665
 
4.0%
1064
 
3.9%
Other values (40)798
49.1%
ValueCountFrequency (%)
012
 
0.7%
322
 
1.4%
627
 
1.7%
722
 
1.4%
922
 
1.4%
1064
3.9%
1110
 
0.6%
1212
 
0.7%
1370
4.3%
1428
 
1.7%
ValueCountFrequency (%)
564
0.2%
542
 
0.1%
532
 
0.1%
522
 
0.1%
516
0.4%
502
 
0.1%
484
0.2%
476
0.4%
464
0.2%
459
0.6%

OT
Boolean

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
False
1537 
True
 
88
ValueCountFrequency (%)
False1537
94.6%
True88
 
5.4%
2022-09-02T02:19:31.838589image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Roof
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct4
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size106.4 KiB
outdoors
1123 
dome
252 
retractable roof (closed)
218 
retractable roof (open)
 
32

Length

Max length25
Median length8
Mean length9.955692308
Min length4

Characters and Unicode

Total characters16178
Distinct characters18
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowoutdoors
2nd rowoutdoors
3rd rowoutdoors
4th rowoutdoors
5th rowoutdoors

Common Values

ValueCountFrequency (%)
outdoors1123
69.1%
dome252
 
15.5%
retractable roof (closed)218
 
13.4%
retractable roof (open)32
 
2.0%

Length

2022-09-02T02:19:32.009411image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-02T02:19:32.239207image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
outdoors1123
52.8%
dome252
 
11.9%
retractable250
 
11.8%
roof250
 
11.8%
closed218
 
10.3%
open32
 
1.5%

Most occurring characters

ValueCountFrequency (%)
o4371
27.0%
r1873
11.6%
t1623
 
10.0%
d1593
 
9.8%
s1341
 
8.3%
u1123
 
6.9%
e1002
 
6.2%
500
 
3.1%
a500
 
3.1%
l468
 
2.9%
Other values (8)1784
11.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter15178
93.8%
Space Separator500
 
3.1%
Open Punctuation250
 
1.5%
Close Punctuation250
 
1.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o4371
28.8%
r1873
12.3%
t1623
 
10.7%
d1593
 
10.5%
s1341
 
8.8%
u1123
 
7.4%
e1002
 
6.6%
a500
 
3.3%
l468
 
3.1%
c468
 
3.1%
Other values (5)816
 
5.4%
Space Separator
ValueCountFrequency (%)
500
100.0%
Open Punctuation
ValueCountFrequency (%)
(250
100.0%
Close Punctuation
ValueCountFrequency (%)
)250
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin15178
93.8%
Common1000
 
6.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
o4371
28.8%
r1873
12.3%
t1623
 
10.7%
d1593
 
10.5%
s1341
 
8.8%
u1123
 
7.4%
e1002
 
6.6%
a500
 
3.3%
l468
 
3.1%
c468
 
3.1%
Other values (5)816
 
5.4%
Common
ValueCountFrequency (%)
500
50.0%
(250
25.0%
)250
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII16178
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o4371
27.0%
r1873
11.6%
t1623
 
10.0%
d1593
 
9.8%
s1341
 
8.3%
u1123
 
6.9%
e1002
 
6.2%
500
 
3.1%
a500
 
3.1%
l468
 
2.9%
Other values (8)1784
11.0%

Surface
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct8
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size101.5 KiB
grass
737 
grass
248 
fieldturf
186 
fieldturf
176 
astroturf
111 
Other values (3)
167 

Length

Max length10
Median length9
Mean length6.864
Min length5

Characters and Unicode

Total characters11154
Distinct characters17
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowgrass
2nd rowgrass
3rd rowgrass
4th rowgrass
5th rowgrass

Common Values

ValueCountFrequency (%)
grass737
45.4%
grass 248
 
15.3%
fieldturf 186
 
11.4%
fieldturf176
 
10.8%
astroturf111
 
6.8%
matrixturf89
 
5.5%
sportturf60
 
3.7%
a_turf18
 
1.1%

Length

2022-09-02T02:19:32.436781image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-02T02:19:32.684088image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
grass985
60.6%
fieldturf362
 
22.3%
astroturf111
 
6.8%
matrixturf89
 
5.5%
sportturf60
 
3.7%
a_turf18
 
1.1%

Most occurring characters

ValueCountFrequency (%)
s2141
19.2%
r1885
16.9%
a1203
10.8%
f1002
9.0%
g985
8.8%
t900
8.1%
u640
 
5.7%
i451
 
4.0%
434
 
3.9%
l362
 
3.2%
Other values (7)1151
10.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter10702
95.9%
Space Separator434
 
3.9%
Connector Punctuation18
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
s2141
20.0%
r1885
17.6%
a1203
11.2%
f1002
9.4%
g985
9.2%
t900
8.4%
u640
 
6.0%
i451
 
4.2%
l362
 
3.4%
d362
 
3.4%
Other values (5)771
 
7.2%
Space Separator
ValueCountFrequency (%)
434
100.0%
Connector Punctuation
ValueCountFrequency (%)
_18
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin10702
95.9%
Common452
 
4.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
s2141
20.0%
r1885
17.6%
a1203
11.2%
f1002
9.4%
g985
9.2%
t900
8.4%
u640
 
6.0%
i451
 
4.2%
l362
 
3.4%
d362
 
3.4%
Other values (5)771
 
7.2%
Common
ValueCountFrequency (%)
434
96.0%
_18
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII11154
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
s2141
19.2%
r1885
16.9%
a1203
10.8%
f1002
9.0%
g985
8.8%
t900
8.1%
u640
 
5.7%
i451
 
4.0%
434
 
3.9%
l362
 
3.2%
Other values (7)1151
10.3%

Temperature
Real number (ℝ≥0)

HIGH CORRELATION

Distinct73
Distinct (%)4.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean62.78584615
Minimum7
Maximum93
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.8 KiB
2022-09-02T02:19:32.915791image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile35
Q152
median72
Q372
95-th percentile82
Maximum93
Range86
Interquartile range (IQR)20

Descriptive statistics

Standard deviation15.27771395
Coefficient of variation (CV)0.2433305415
Kurtosis-0.1185534416
Mean62.78584615
Median Absolute Deviation (MAD)8
Skewness-0.7708927782
Sum102027
Variance233.4085434
MonotonicityNot monotonic
2022-09-02T02:19:33.156152image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
72550
33.8%
4834
 
2.1%
5232
 
2.0%
5430
 
1.8%
8129
 
1.8%
4328
 
1.7%
7628
 
1.7%
5928
 
1.7%
4626
 
1.6%
6426
 
1.6%
Other values (63)814
50.1%
ValueCountFrequency (%)
72
 
0.1%
114
0.2%
142
 
0.1%
152
 
0.1%
232
 
0.1%
242
 
0.1%
256
0.4%
262
 
0.1%
282
 
0.1%
292
 
0.1%
ValueCountFrequency (%)
934
 
0.2%
914
 
0.2%
902
 
0.1%
891
 
0.1%
888
0.5%
8713
0.8%
868
0.5%
8518
1.1%
847
 
0.4%
8314
0.9%

Humidity
Real number (ℝ≥0)

HIGH CORRELATION

Distinct85
Distinct (%)5.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean55.72061538
Minimum0
Maximum100
Zeros2
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size12.8 KiB
2022-09-02T02:19:33.405540image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile35.2
Q145
median48
Q368
95-th percentile88
Maximum100
Range100
Interquartile range (IQR)23

Descriptive statistics

Standard deviation16.97849189
Coefficient of variation (CV)0.3047075444
Kurtosis-0.004525211045
Mean55.72061538
Median Absolute Deviation (MAD)8
Skewness0.4281067826
Sum90546
Variance288.2691868
MonotonicityNot monotonic
2022-09-02T02:19:33.655001image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
45544
33.5%
5634
 
2.1%
4630
 
1.8%
5229
 
1.8%
6028
 
1.7%
6628
 
1.7%
5827
 
1.7%
6725
 
1.5%
5924
 
1.5%
5724
 
1.5%
Other values (75)832
51.2%
ValueCountFrequency (%)
02
 
0.1%
72
 
0.1%
82
 
0.1%
93
0.2%
102
 
0.1%
112
 
0.1%
122
 
0.1%
131
 
0.1%
146
0.4%
194
0.2%
ValueCountFrequency (%)
1004
 
0.2%
994
 
0.2%
972
 
0.1%
952
 
0.1%
944
 
0.2%
9313
0.8%
9210
0.6%
9114
0.9%
9012
0.7%
8911
0.7%

Wind_Speed
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct28
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.851692308
Minimum0
Maximum35
Zeros589
Zeros (%)36.2%
Negative0
Negative (%)0.0%
Memory size12.8 KiB
2022-09-02T02:19:33.880761image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median5
Q310
95-th percentile17
Maximum35
Range35
Interquartile range (IQR)10

Descriptive statistics

Standard deviation6.002424482
Coefficient of variation (CV)1.025758732
Kurtosis0.4715231197
Mean5.851692308
Median Absolute Deviation (MAD)5
Skewness0.9059521431
Sum9509
Variance36.02909966
MonotonicityNot monotonic
2022-09-02T02:19:34.094659image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
0589
36.2%
8108
 
6.6%
793
 
5.7%
487
 
5.4%
685
 
5.2%
573
 
4.5%
968
 
4.2%
1064
 
3.9%
1261
 
3.8%
1159
 
3.6%
Other values (18)338
20.8%
ValueCountFrequency (%)
0589
36.2%
124
 
1.5%
232
 
2.0%
346
 
2.8%
487
 
5.4%
573
 
4.5%
685
 
5.2%
793
 
5.7%
8108
 
6.6%
968
 
4.2%
ValueCountFrequency (%)
352
 
0.1%
272
 
0.1%
252
 
0.1%
244
 
0.2%
238
 
0.5%
228
 
0.5%
216
 
0.4%
206
 
0.4%
1924
1.5%
1812
0.7%

Vegas_Line
Real number (ℝ)

Distinct39
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-5.660923077
Minimum-22
Maximum0
Zeros4
Zeros (%)0.2%
Negative1621
Negative (%)99.8%
Memory size12.8 KiB
2022-09-02T02:19:34.318885image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum-22
5-th percentile-13
Q1-7.5
median-4.5
Q3-3
95-th percentile-1
Maximum0
Range22
Interquartile range (IQR)4.5

Descriptive statistics

Standard deviation3.676887322
Coefficient of variation (CV)-0.6495208064
Kurtosis1.369975557
Mean-5.660923077
Median Absolute Deviation (MAD)2
Skewness-1.17694802
Sum-9199
Variance13.51950038
MonotonicityNot monotonic
2022-09-02T02:19:34.537997image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
-3215
13.2%
-3.5163
 
10.0%
-7114
 
7.0%
-2.5112
 
6.9%
-1100
 
6.2%
-489
 
5.5%
-7.586
 
5.3%
-6.578
 
4.8%
-672
 
4.4%
-5.570
 
4.3%
Other values (29)526
32.4%
ValueCountFrequency (%)
-222
 
0.1%
-20.52
 
0.1%
-202
 
0.1%
-183
 
0.2%
-17.55
0.3%
-176
0.4%
-16.512
0.7%
-162
 
0.1%
-15.54
 
0.2%
-154
 
0.2%
ValueCountFrequency (%)
04
 
0.2%
-1100
6.2%
-1.548
 
3.0%
-240
 
2.5%
-2.5112
6.9%
-3215
13.2%
-3.5163
10.0%
-489
5.5%
-4.546
 
2.8%
-552
 
3.2%

Vegas_Favorite
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct33
Distinct (%)2.0%
Missing4
Missing (%)0.2%
Memory size95.3 KiB
KAN
 
104
LAR
 
86
GNB
 
83
TAM
 
81
BAL
 
77
Other values (28)
1190 

Length

Max length19
Median length3
Mean length3.059222702
Min length3

Characters and Unicode

Total characters4959
Distinct characters34
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCHI
2nd rowCHI
3rd rowLAR
4th rowLAR
5th rowCLE

Common Values

ValueCountFrequency (%)
KAN104
 
6.4%
LAR86
 
5.3%
GNB83
 
5.1%
TAM81
 
5.0%
BAL77
 
4.7%
NOR72
 
4.4%
NWE72
 
4.4%
DAL71
 
4.4%
SFO69
 
4.2%
SEA68
 
4.2%
Other values (23)838
51.6%

Length

2022-09-02T02:19:34.765969image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
kan104
 
6.4%
lar86
 
5.3%
gnb83
 
5.1%
tam81
 
5.0%
bal77
 
4.7%
nor72
 
4.4%
nwe72
 
4.4%
dal71
 
4.3%
sfo69
 
4.2%
sea68
 
4.2%
Other values (25)850
52.1%

Most occurring characters

ValueCountFrequency (%)
A718
14.5%
N591
11.9%
L425
 
8.6%
I349
 
7.0%
E312
 
6.3%
R285
 
5.7%
T240
 
4.8%
B226
 
4.6%
C220
 
4.4%
O177
 
3.6%
Other values (24)1416
28.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter4875
98.3%
Lowercase Letter60
 
1.2%
Space Separator12
 
0.2%
Connector Punctuation6
 
0.1%
Dash Punctuation6
 
0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A718
14.7%
N591
12.1%
L425
 
8.7%
I349
 
7.2%
E312
 
6.4%
R285
 
5.8%
T240
 
4.9%
B226
 
4.6%
C220
 
4.5%
O177
 
3.6%
Other values (14)1332
27.3%
Lowercase Letter
ValueCountFrequency (%)
a12
20.0%
b12
20.0%
e12
20.0%
t6
10.0%
v6
10.0%
r6
10.0%
m6
10.0%
Space Separator
ValueCountFrequency (%)
12
100.0%
Connector Punctuation
ValueCountFrequency (%)
_6
100.0%
Dash Punctuation
ValueCountFrequency (%)
-6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin4935
99.5%
Common24
 
0.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
A718
14.5%
N591
12.0%
L425
 
8.6%
I349
 
7.1%
E312
 
6.3%
R285
 
5.8%
T240
 
4.9%
B226
 
4.6%
C220
 
4.5%
O177
 
3.6%
Other values (21)1392
28.2%
Common
ValueCountFrequency (%)
12
50.0%
_6
25.0%
-6
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII4959
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A718
14.5%
N591
11.9%
L425
 
8.6%
I349
 
7.0%
E312
 
6.3%
R285
 
5.7%
T240
 
4.8%
B226
 
4.6%
C220
 
4.4%
O177
 
3.6%
Other values (24)1416
28.6%

Over_Under
Real number (ℝ≥0)

HIGH CORRELATION

Distinct44
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean46.61107692
Minimum35
Maximum58
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.8 KiB
2022-09-02T02:19:34.988121image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum35
5-th percentile40
Q143.5
median46.5
Q349.5
95-th percentile54.5
Maximum58
Range23
Interquartile range (IQR)6

Descriptive statistics

Standard deviation4.263539763
Coefficient of variation (CV)0.09147052686
Kurtosis-0.3061021445
Mean46.61107692
Median Absolute Deviation (MAD)3
Skewness0.1249977772
Sum75743
Variance18.17777131
MonotonicityNot monotonic
2022-09-02T02:19:35.245678image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
4494
 
5.8%
4788
 
5.4%
4878
 
4.8%
4674
 
4.6%
46.573
 
4.5%
4972
 
4.4%
4571
 
4.4%
4368
 
4.2%
44.565
 
4.0%
49.563
 
3.9%
Other values (34)879
54.1%
ValueCountFrequency (%)
352
 
0.1%
36.58
0.5%
3714
0.9%
37.512
0.7%
387
 
0.4%
38.56
 
0.4%
3910
0.6%
39.518
1.1%
4016
1.0%
40.514
0.9%
ValueCountFrequency (%)
582
 
0.1%
57.52
 
0.1%
56.512
0.7%
564
 
0.2%
55.520
1.2%
5522
1.4%
54.528
1.7%
5416
1.0%
53.520
1.2%
5312
0.7%

game_date
Categorical

HIGH CARDINALITY

Distinct174
Distinct (%)10.7%
Missing0
Missing (%)0.0%
Memory size106.4 KiB
2021-01-03
 
32
2019-12-29
 
32
2022-01-02
 
30
2021-10-10
 
29
2020-09-27
 
28
Other values (169)
1474 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters16250
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row2019-09-05
2nd row2019-09-05
3rd row2019-09-08
4th row2019-09-08
5th row2019-09-08

Common Values

ValueCountFrequency (%)
2021-01-0332
 
2.0%
2019-12-2932
 
2.0%
2022-01-0230
 
1.8%
2021-10-1029
 
1.8%
2020-09-2728
 
1.7%
2019-12-1528
 
1.7%
2020-09-2028
 
1.7%
2022-01-0928
 
1.7%
2019-12-0828
 
1.7%
2019-09-2228
 
1.7%
Other values (164)1334
82.1%

Length

2022-09-02T02:19:35.487472image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2021-01-0332
 
2.0%
2019-12-2932
 
2.0%
2022-01-0230
 
1.8%
2021-10-1029
 
1.8%
2020-09-2728
 
1.7%
2019-12-1528
 
1.7%
2020-09-2028
 
1.7%
2022-01-0928
 
1.7%
2019-12-0828
 
1.7%
2019-09-2228
 
1.7%
Other values (164)1334
82.1%

Most occurring characters

ValueCountFrequency (%)
24006
24.7%
03605
22.2%
13402
20.9%
-3250
20.0%
91013
 
6.2%
3224
 
1.4%
7176
 
1.1%
5161
 
1.0%
8154
 
0.9%
6137
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number13000
80.0%
Dash Punctuation3250
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
24006
30.8%
03605
27.7%
13402
26.2%
91013
 
7.8%
3224
 
1.7%
7176
 
1.4%
5161
 
1.2%
8154
 
1.2%
6137
 
1.1%
4122
 
0.9%
Dash Punctuation
ValueCountFrequency (%)
-3250
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common16250
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
24006
24.7%
03605
22.2%
13402
20.9%
-3250
20.0%
91013
 
6.2%
3224
 
1.4%
7176
 
1.1%
5161
 
1.0%
8154
 
0.9%
6137
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII16250
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
24006
24.7%
03605
22.2%
13402
20.9%
-3250
20.0%
91013
 
6.2%
3224
 
1.4%
7176
 
1.1%
5161
 
1.0%
8154
 
0.9%
6137
 
0.8%

Interactions

2022-09-02T02:19:18.658592image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T02:18:27.519333image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T02:18:30.986869image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T02:18:34.485535image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T02:18:38.027460image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T02:18:41.515828image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T02:18:44.912752image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T02:18:48.282423image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T02:18:51.624129image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T02:18:54.961305image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T02:18:58.387482image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T02:19:01.916796image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T02:19:05.283744image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T02:19:08.570463image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T02:19:11.891497image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T02:19:15.310782image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T02:19:18.872998image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T02:18:27.734644image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T02:18:31.222404image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T02:18:34.717527image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T02:18:38.266183image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T02:18:41.736023image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T02:18:45.145442image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T02:18:48.488662image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T02:18:51.841528image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T02:18:55.175176image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T02:18:58.616913image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T02:19:02.161086image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T02:19:05.503710image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T02:19:08.776288image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T02:19:12.105905image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T02:19:15.528363image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T02:19:19.092428image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T02:18:27.954142image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T02:18:31.439476image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T02:18:34.965073image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T02:18:38.480117image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T02:18:41.966617image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T02:18:45.366473image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T02:18:48.695966image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T02:18:52.042826image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T02:18:55.389969image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T02:18:58.836196image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T02:19:02.384898image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T02:19:05.715435image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T02:19:08.986639image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T02:19:12.314774image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T02:19:15.740110image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T02:19:19.327177image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T02:18:28.176941image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T02:18:31.663994image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T02:18:35.246793image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T02:18:38.704395image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T02:18:42.186072image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T02:18:45.591026image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T02:18:48.903407image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T02:18:52.253900image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T02:18:55.603992image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T02:18:59.070505image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T02:19:02.606119image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T02:19:05.948731image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T02:19:09.208832image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T02:19:12.534031image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T02:19:15.959192image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T02:19:19.563556image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T02:18:28.396214image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T02:18:31.886991image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T02:18:35.461365image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T02:18:38.921530image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T02:18:42.411933image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T02:18:45.805393image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T02:18:49.115043image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T02:18:52.461733image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T02:18:55.833062image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T02:18:59.305824image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T02:19:02.828516image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T02:19:06.170616image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T02:19:09.423407image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T02:19:12.748420image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T02:19:16.178736image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T02:19:19.769976image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T02:18:28.600799image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T02:18:32.122277image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T02:18:35.692499image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
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Correlations

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Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-09-02T02:19:36.069634image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-09-02T02:19:36.424632image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-09-02T02:19:36.748308image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-09-02T02:19:37.083481image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

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A simple visualization of nullity by column.
2022-09-02T02:19:23.399790image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
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The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

game_idOff_abbrevDef_abbrevfgafgmxpaxpmfga_0_39fgm_0_39fga_40_49fgm_40_49fga_50fgm_50playerTotal_DKPTotal_FDPTotal_SDPvis_teamhome_teamvis_scorehome_scoreOTRoofSurfaceTemperatureHumidityWind_SpeedVegas_LineVegas_FavoriteOver_Undergame_date
0201909050chiCHIGNB1100110000Eddy Pineiro333GNBCHI103Falseoutdoorsgrass656910-3.5CHI47.02019-09-05
1201909050chiGNBCHI1111110000Mason Crosby444GNBCHI103Falseoutdoorsgrass656910-3.5CHI47.02019-09-05
2201909080carCARLAR3233001121Joey Slye121212LARCAR3027Falseoutdoorsgrass87533-1.5LAR49.52019-09-08
3201909080carLARCAR4333112111Greg Zuerlein151515LARCAR3027Falseoutdoorsgrass87533-1.5LAR49.52019-09-08
4201909080cleTENCLE2255110011Cairo Santos131313TENCLE4313Falseoutdoorsgrass715510-5.5CLE44.02019-09-08
5201909080cle000021000000Austin Seibert111TENCLE4313Falseoutdoorsgrass715510-5.5CLE44.02019-09-08
6201909080crdARIDET4411331100Zane Gonzalez141414DETARI2727Trueretractable roof (closed)grass72450-2.5DET45.52019-09-08
7201909080crdDETARI2233110011Matt Prater111111DETARI2727Trueretractable roof (closed)grass72450-2.5DET45.52019-09-08
8201909080dal000055000000Brett Maher555NYGDAL1735Falseretractable roof (closed)fieldturf72450-7.0DAL44.02019-09-08
9201909080dalNYGDAL1122110000Aldrick Rosas555NYGDAL1735Falseretractable roof (closed)fieldturf72450-7.0DAL44.02019-09-08

Last rows

game_idOff_abbrevDef_abbrevfgafgmxpaxpmfga_0_39fgm_0_39fga_40_49fgm_40_49fga_50fgm_50playerTotal_DKPTotal_FDPTotal_SDPvis_teamhome_teamvis_scorehome_scoreOTRoofSurfaceTemperatureHumidityWind_SpeedVegas_LineVegas_FavoriteOver_Undergame_date
1615202201230kan000044000000Tyler Bass444BUFKAN3642Trueoutdoorsgrass35546-2.0KAN54.52022-01-23
1616202201230kanKANBUF4343221110Harrison Butker131313BUFKAN3642Trueoutdoorsgrass35546-2.0KAN54.52022-01-23
1617202201230tamTAMLAR3233000000Ryan Succop333LARTAM3027Falseoutdoorsgrass506911-3.0TAM48.02022-01-23
1618202201230tamLARTAM4333331000Matt Gay121212LARTAM3027Falseoutdoorsgrass506911-3.0TAM48.02022-01-23
1619202201300kanCINKAN4411330011Evan McPherson151515CINKAN2724Trueoutdoorsgrass41414-7.0KAN55.02022-01-30
1620202201300kanKANCIN1133001100Harrison Butker777CINKAN2724Trueoutdoorsgrass41414-7.0KAN55.02022-01-30
1621202201300ramLARSFO3222111110Matt Gay999SFOLAR1720Falsedomematrixturf72450-3.5LAR46.02022-01-30
1622202201300ramSFOLAR1122000000Robbie Gould222SFOLAR1720Falsedomematrixturf72450-3.5LAR46.02022-01-30
1623202202130cinCINLAR2222000000Evan McPherson222LARCIN2320Falsedomematrixturf72450-4.0LAR48.52022-02-13
1624202202130cinLARCIN1122001100Matt Gay666LARCIN2320Falsedomematrixturf72450-4.0LAR48.52022-02-13